code: def forward(self, x, h):
      # Embed word ids to vectors
      x = self.embed(x)
  
      # Forward propagate LSTM
      out, (h, c) = self.lstm(x, h)
  
      # Reshape output to (batch_size*sequence_length, hidden_size)
      out = out.reshape(out.size(0) * out.size(1), out.size(2))
  
      # Decode hidden states of all time steps
      out = self.linear(out)
      return out, (h, c)
  
file_path: pytorch-tutorial/tutorials/02-intermediate/language_model/main.py
imports: import torch
  import torch.nn
  import numpy
  from torch.nn.utils import clip_grad_norm_
  from data_utils import Dictionary, Corpus
label_desc: Use image node_id 2 for calling the RNNLM obj's underlying member method code with example usage: obj.forward(x, h) and returns: out,  h,  c